Systems and methods herein generally relate to scanner calibration and more particularly to scanner calibration processes that transform scanned colors into device-independent color space.
In order to achieve high quality reproduction, it is often advantageous to calibrate optical scanners. Additionally, scanner output (which is often detected as combinations of red, green, and blue (RGB)) into a more precise, generic, device-independent format (such as HSV, CIE XYZ, and CIE L*a*b* values) that can be more easily utilized by all devices. For example, instead of transferring color signals in the RGB space from a scanner directly to color signals in the cyan magenta yellow black (CMYK) space to a color printer, a device-independent color space is commonly used as the intermediate color space for other evolved image processing, such as compression, decompression, enhancement, correction, and the like.
In general, traditional methods of scanner profiling are used to transform the color data (e.g., obtained from a scanner or a linear array sensor) in the device dependent color space to the device independent color space. These traditional methods of scanner profiling use, for example, a matrix or a look-up table (LUT). A 3×3 matrix relates a RGB input to a L*a*b* or XYZ output, with a linear in reflectance like RGB, and converting XYZ output to L*a*b* using standard formulas. Further improvements may be obtained by using a 4×3 matrix, where the additional row represents the offset terms. A look-up table transformation maps a RGB input to a L*a*b* output and often involves interpolation using available LUT entries.
Therefore, much work has been directed toward achieving high accuracy color space transformation. Often, in scanner device RGB to CIE XYZ conversion, a one-dimensional (1D) gray balancing look-up table (LUT) is applied to the input RGB, followed by a matrix conversion to transform the adjusted RGB to CIE XYZ. Such methods are able to produce incremental improvement in the quality of color space transformation, but in order to keep products competitive in terms of cost, quality and performance further improve in color accuracy (without restoring to computationally expensive algorithms) would be useful.